---------- Forwarded message ----------
Date: Thu, 3 Mar 2011 11:50:24 +0100
From: Paola Cecchi <paola.cecchi(a)df.unipi.it>
To: dottorandi(a)mail.dm.unipi.it, dottorandi(a)di.unipi
Cc: degano(a)di.unipi.it, broglia(a)dm.unipi.it
Subject: Seminario Scuola Galilei
SEMINARIO GALILEIANO
===========================
Prof.ri Fosca Giannotti(1) e Dino Pedreschi(2)
"Mobility Data Analysis and Mining: Understanding Human Movement Patterns
from Trajectory Data"
Abstract: Uncovering the patterns of human mobility, which characterize the
trajectories humans follow during their daily activity, is not only a major
intellectual challenge, but also of importance for urban planning,
transportation engineering, public health, and economic forecasting.
Recently, the availability of mobile-phone records,
global-positioning-system data and other mobility-related big data capturing
aspects of human mobility are providing a new powerful social microscope,
and have given empirically driven momentum to the subject. Based on these
data, a new multidisciplinary research area is emerging at the crossroads of
mobility, data mining, statistical modeling, and privacy. The seminar
assesses this research frontier by providing an account on the models of
human mobility recently developed by network scientists and statistical
physicists, as well as on the methods for mobility data mining, such as
trajectory pattern mining and trajectory clustering, developed by data
mining researchers. We illustrate the key results of a European-wide
research project called GeoPKDD, Geographic Privacy-Aware Knowledge
Discovery and Delivery, which created an integrated platform for complex
analysis of mobility data, and show its analytical power in unvealing the
complexity of urban mobility in a large scale experiment, based on a massive
real life GPS dataset, obtained from 17,000 vehicles with on-board GPS
receivers, tracked during one week of ordinary mobile activity in the city
of Milan, Italy. We argue how statistical modeling and computational
sciences are converging towards a data science that, powered by the big data
of ICT-mediated human activities, is aiming at a quantitative understanding
of social phenomena. We conclude with an example of how the combined methods
of data mining and network science can provide deeper insight into the
interplay between human mobility and the social network, and how the
movement behavior of people impacts the dynamics of social ties.
Martedi 8/3/2011 - ore 15:00
Aula 131 - piano terra - Ed. C
Dipartimento di Fisica "E.Fermi"
(1) Knowledge Discovery and Data Mining Laboratory KDD LAB, Istituto di
Scienza e Tecnologie dell'Informazione "A. Faedo" del CNR, Pisa
(2) Knowledge Discovery and Data Mining Laboratory KDD LAB, Dipartimento di
Informatica, Università di Pisa
--
Paola Cecchi
Secretary Theory Division
and
Secretary of Graduate Course in Physics
Department of Physics
University of Pisa
Edificio C - Largo B.Pontecorvo, 3
56127 PISA
Tel. +39 050 2214 888
Fax +39 050 2214 887
e-mail: paola.cecchi(a)df.unipi.it
_________________________________________